Research Projects

Many-Layered Learning

Our interests are rooted in the longstanding fundamental problem of
how intelligent systems can acquire features, terms, and other
representational structures that are prerequisite to further
learning. Simple learning can be accomplished by application of a
statistical pattern-finding algorithms, but how are learned
patterns employed in the grander design of sustained learning over
the lifetime of an intelligent entity? In terms of Clark &
Thornton's compelling distinction between Type-1 and Type-2
learning, we are intrigued with the issues involved in
understanding and modeling Type-2 learning, which involves building
representational mappings when no statistical relationship exists
between the independent and dependent variables. Quartz has also
described well this dichotomy between setting up representations
(Type-2) and then making use of those representations for the
better understood processes of identifying simple input/output
mappings (Type-1). What mechanisms can account for sustained
Type-2 and Type-1 learning? An understanding of these issues is
central to attaining new levels of mechanical intelligence.

We have been working on algorithms that incidentally solve Type-1
problems when present and possible, each one producing a building
block that joins a many-layered deeply-nested organization of such
elements. Earlier learning lays the groundwork for later learning.
We are guided by the principle that all learning is simple if the
prerequisites are in place. We are examining learning from
textbooks, as each text presents a large body of coherent knowledge
carefully organized in terms of building blocks and their
dependencies. This will be our direction for at least the next
several years.

Brute Force Goal Regression

We are developing a method that applies goal regression in a brute
force manner to solve a problem domain. The result is a policy,
expressed as a list of decision rules. From these rules, we are
examining how to extract features that allow the rules to be
compressed.

Classification of Plankton Images

UMass researchers from the Computer Vision Laboaratory
and the Machine Learning Laboratory are collaborating with marine
scientists from Bigelow
Laboratory to build image classification systems that are
capable of discriminating a large variety of plankton. The ability
to automate analyses of samples in various settings will enable
studies of such ocean life to be conducted at much lower cost, and
in a much more timely manner. We are investigating classification
algorithms, ensemble classifiers, and feature sets that enable
separation of useful classes to apply to challenging real-world
problems.

Music Perception

One would like to be able to take as input an audio signal of a
music performance, and produce as output a music score for the
selection that was performed. There are many subproblems,
including determining the fundamental pitch events, pulse, meter,
key signature, intentional variations of pitch and note duration,
articulation identification, voice leading and chord
identification. In addition to a score, one would like to produce
a variety of analyses of a piece of music, such as a grammatical
analysis that captures the structure underlying the composition.

ITI Decision Tree Software

We have developed a variety of decision tree induction algorithms
over the years, including the ITI
(Incremental Tree Inducer) and DMTI (Direct Metric Tree
Induction) decision tree induction systems.